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The distributive Effects of Education: An Unconditional Quantile Regression Approach

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Author Info

  • Javier Alejo

    (Universidad Nacional de La Plata and CONICET)

  • María Florencia Gabrielli

    (Universidad Nacional de Cuyo and CONICET)

  • Walter Sosa Escudero

    (Universidad de San Andres and CONICET)

Abstract

En este trabajo utilizamos los más recientes métodos de regresión no condicionada por cuantiles (RNCQ) para estudiar los efectos distributivos de la educación en Argentina. Los métodos estándar se centran, por lo general, en efectos promedio o estudian los efectos distributivos ya sea haciendo uso de suposiciones estrictas al modelar y/o a través de descomposiciones contrafácticas que requieren varias observaciones temporales. La aplicación empírica en este trabajo muestra la flexibilidad y utilidad de los métodos de RNCQ. Nuestra aplicación para el caso de Argentina sugiere que la educación ha contribuido positivamente al aumento de la desigualdad, debido principalmente a efectos fuertemente heterogéneos de la educación sobre los ingresos.

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File URL: http://cedlas.econo.unlp.edu.ar/archivos_upload/doc_cedlas125.pdf
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Bibliographic Info

Paper provided by CEDLAS, Universidad Nacional de La Plata in its series CEDLAS, Working Papers with number 0125.

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Length: 24 pages
Date of creation: Nov 2011
Date of revision:
Handle: RePEc:dls:wpaper:0125

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Postal: Calle 48 No555 - La Plata (1900)
Phone: 21- 1466
Fax: 54-21-25-9536
Web page: http://cedlas.econo.unlp.edu.ar/
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Related research

Keywords: unconditional quantile regression; income inequality; education; Argentina;

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References

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  1. Sergio Firpo & Nicole M. Fortin & Thomas Lemieux, 2007. "Unconditional Quantile Regressions," NBER Technical Working Papers 0339, National Bureau of Economic Research, Inc.
  2. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  3. Melly, Blaise, 2005. "Decomposition of differences in distribution using quantile regression," Labour Economics, Elsevier, vol. 12(4), pages 577-590, August.
  4. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521608275, April.
  5. David Card, 2000. "Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems," NBER Working Papers 7769, National Bureau of Economic Research, Inc.
  6. Javier Alejo, 2006. "Desigualdad Salarial en el Gran Buenos Aires: Una Aplicación de Regresión por Cuantiles en Microdescomposiciones," CEDLAS, Working Papers 0036, CEDLAS, Universidad Nacional de La Plata.
  7. Martins, Pedro S. & Pereira, Pedro T., 2004. "Does education reduce wage inequality? Quantile regression evidence from 16 countries," Labour Economics, Elsevier, vol. 11(3), pages 355-371, June.
  8. José Mata & José A. F. Machado, 2005. "Counterfactual decomposition of changes in wage distributions using quantile regression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 445-465.
  9. Buchinsky, Moshe, 1994. "Changes in the U.S. Wage Structure 1963-1987: Application of Quantile Regression," Econometrica, Econometric Society, vol. 62(2), pages 405-58, March.
  10. Walter Sosa Escudero & Sergio Petralia, 2010. "“I Can Hear the Grass Grow”: The Anatomy of Distributive Changes in Argentina," CEDLAS, Working Papers 0106, CEDLAS, Universidad Nacional de La Plata.
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Cited by:
  1. Deborah A. Cobb-Clark & Sonja C. Kassenboehmer & Mathias G. Sinning, 2013. "Locus of Control and Savings," Ruhr Economic Papers 0455, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.

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